Stacking the real and imaginary parts as an entire real-valued input is the method that most existing deep learning (DL)-based approaches adopt for complex-valued CSI feedback [1]}, [2]}-[3]}, [4]}, [5]}. However, the correlation similarity established in Theorem 1 makes it feasible that only the real part of the CSI matrix is trained while the trained network can be directly applied to the imaginary part. With the input and output size reduced to half of the stacked CSI, the total number of network parameters can be saved by at least a half, resulting in a lightweight network with better performance that is easier to train.
